Nuclear cardiology in the literature: A selection of recent, original research papers
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Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT. A Multicenter Study
Julian Betancur, Frederic Commandeur, Mahsaw Motlagh, Tali Sharir,
Andrew J. Einstein, Sabahat Bokhari, Mathews B. Fish, Terrence D. Ruddy, Philipp Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Guido Germano, Yuka Otaki, Balaji K. Tamarappoo, Damini Dey, Daniel S. Berman and Piotr J. Slomka.
Los Angeles, CA
J Am Coll Cardiol Img 2018;11:1654–63
Context: Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI).
Methods and Results: This study evaluated the automatic prediction of obstructive coronary artery disease (CAD) from SPECT MPI by deep learning as compared with total perfusion deficit (TPD). In this retrospective study,...
Author has nothing to disclose.